Jeju Island, Korea
October 4-8, 2004

Compact Acoustic Model for Embedded Implementation

Junho Park, Hanseok Ko

Korea University, Seoul, Korea, Korea

An acoustic model for an embedded speech recognition system must
exhibit two desirable features; ability to minimize performance
degradation in recognition while solving the memory problem under
limited system resources. To cope with the challenges, we introduce
the state-clustered tied-mixture (SCTM) HMM as an acoustic model
optimization. The proposed SCTM modeling shows a significant
improvement in recognition performance as well as a solution to
sparse training data problem. Moreover, the state weight quantizing
method achieves a drastic reduction in model size. In this paper, we
describe the acoustic model optimization procedure for embedded
speech recognition system and corresponding performance evaluation
results.